Framework for marketplace analysis

ABSTRACT

A marketplace diagnostics framework for analyzing and managing online marketplaces.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority fromco-pending U.S. patent application Ser. No. 14/936,021, filed Nov. 9,2015, entitled FRAMEWORK FOR MARKETPLACE ANALYSIS, which is acontinuation of and claims priority from U.S. patent application Ser.No. 13/538,902, filed Jun. 29, 2012, issued as U.S. Pat. No. 9,183,564on Nov. 10, 2015, and entitled FRAMEWORK FOR MARKETPLACE ANALYSIS, thecontents of each of which is hereby incorporated by reference.

BACKGROUND 1. Field

Example embodiments relate to marketplace analysis.

2. Description of the Related Art

Online marketplaces have been emerging for the last two decades. Onlinemarketplaces, such as eBay, Amazon, and Yahoo have provided an arena topurchase and advertise various products and services. For example,billions of dollars are now being spent per year on online advertising,and there seems to be no end to the growth of the Internet or onlineadvertising.

Although companies have achieved great success through onlinemarketplaces, the scale of large online marketplaces makes themdifficult to analyze and manage efficiently. For example, one tradeoffcan be the complexity of determining pricing and potential revenuederived from an online advertisement. Another complexity can beunderstanding a structure of a marketplace and how various marketplacesmay be interrelated. This is especially difficult, considering structureof an online marketplace may constantly change in unpredictable ways.Because of the abovementioned complexities and other issues that makeanalyzing and managing of online marketplaces difficult, users andoperators of such marketplaces would benefit from an effective frameworkfor analyzing and managing online marketplaces.

SUMMARY

A system, such as a marketplace diagnostics framework (MDF), formanaging an online marketplace that may be configured to identify afault diagnosis technique for an online marketplace, where the faultdiagnosis technique may include procedures and measurements fordiagnosing an issue in the online marketplace. Prior to theidentification of the fault diagnosis technique, the system may beconfigured to search for an appropriate fault diagnosis technique for aparticular online marketplace.

The system may also be configured to evaluate the procedures and themeasurements of the fault diagnosis technique for their effectiveness indiagnosing an issue in the online marketplace. Further included may beprioritizing the procedures and the measurements of the fault diagnosistechnique according to at least the evaluation of the procedures and themeasurements. Also included may be executing at least one of theprocedures and the measurements of the fault diagnosis techniqueaccording to the prioritization of the procedures and the measurements.

The system may also be configured to: store diagnostic informationresulting from the execution of the at least one of the procedures andthe measurements; generate a remedy according to at least the diagnosticinformation; and execute the remedy. Furthermore, the system may beconfigured to evaluate remediation information resulting from theexecution of the remedy. Further included may be storing the remediationinformation and the evaluation of the remediation information; and thencommunicating the remediation information and the evaluation of theremediation information to a user or operator of the online marketplace,the system, or another system.

BRIEF DESCRIPTION OF THE DRAWINGS

The system may be better understood with reference to the followingdrawings and description. Non-limiting and non-exhaustive embodimentsare described with reference to the following drawings. The componentsin the drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating the principles of the invention. In thedrawings, like referenced numerals designate corresponding partsthroughout the different views.

FIG. 1 illustrates a block diagram of one embodiment of a network thatcan implement one embodiment of a marketplace diagnostics framework(MDF) for managing and analyzing an online marketplace;

FIG. 2 illustrates a block diagram of one embodiment of an electronicdevice that can implement an aspect of one embodiment of an MDF; and

FIG. 3 illustrates a flowchart of an example method that can beperformed by an electronic device, such an application server or thedevice of FIG. 2.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The scale of online marketplaces makes such marketplaces difficult toanalyze and manage efficiently. Further, the scale makes remedyingissues in an online marketplace problematic. The analysis and managementof an online marketplace is difficult because online marketplacestypically change unpredictably, and sometimes the structure of theonline marketplace is not well understood. Using one analogy, an onlinemarketplace may be as complex as a patient's anatomy and physiology.Therefore, a system analogous to a system for diagnosing a patient maybe beneficial to parties that desire to analyze and manage an onlinemarketplace. Further, it may be beneficial if such a system is automatedvia a computer information system, for example.

Described herein is a system, such as a marketplace diagnosticsframework (also referred to herein as the MDF), for analyzing andmanaging an online marketplace. Further, the system provides fordetermining procedures and measurements for diagnosing issues in anonline marketplace, and for determining remedies for, at least,alleviating the issues.

In one embodiment, a computer implementing the MDF may perform a methodfor analyzing and managing an online marketplace. The method foranalyzing and managing an online marketplace may include identifying afault diagnosis technique for an online marketplace, where the faultdiagnosis technique may include procedures and measurements fordiagnosing an issue in the online marketplace. Prior to theidentification of the fault diagnosis technique, the method may includesearching for an appropriate fault diagnosis technique for a particularonline marketplace. The method may also include evaluating theprocedures and the measurements of the fault diagnosis technique fortheir effectiveness in diagnosing an issue in the online marketplace.Further included may be prioritizing the procedures and the measurementsof the fault diagnosis technique according to at least the evaluation ofthe procedures and the measurements. Also included may be executing atleast one of the procedures and the measurements of the fault diagnosistechnique according to the prioritization of the procedures and themeasurements. The method may also include storing diagnostic informationresulting from the execution of the at least one of the procedures andthe measurements; generating a remedy according to at least thediagnostic information; and executing the remedy.

Furthermore, the method may include evaluating remediation informationresulting from the execution of the remedy. Subsequently, the method mayinclude storing the remediation information and the evaluation of theremediation information; and then communicating the remediationinformation and the evaluation of the remediation information to a useror an operator of the online marketplace or the MDF, the MDF, anothersystem, or another computer, for example. In one embodiment, feedbackmay be provided to the MDF, aspects of the MDF, users of the MDF, and/orother systems besides the MDF. Such feedback may facilitate automationof control of the marketplace and/or the MDF.

In one embodiment, the prioritization of the procedures and themeasurements includes determining a most effective procedure,measurement, and/or indicator for determining an issue occurring in theonline marketplace, and ranking the most effective procedure,measurement, and/or indicator higher than other procedures,measurements, and/or indicators of the fault diagnosis technique,respectively.

Also, in one embodiment, at least one of the procedures of the faultdiagnosis technique may include at least one of the measurements of thefault diagnosis technique, and at least one of the measurements of thefault diagnosis technique may include a measurement of an indicator fordetermining an issue occurring in the online marketplace and/or a systemrelated to the online marketplace. Further, at least one of theprocedures of the fault diagnosis technique may include statisticalmodeling for at least one of the measurements of the fault diagnosistechnique. Also, a procedure of the fault diagnosis technique mayinclude a procedure for classifying an overall health state of theonline marketplace and/or a procedure for classifying a diagnosed issuewith respect to an effect of the diagnosed issue on the onlinemarketplace. For example, the procedure for classifying the overallhealth state of the online marketplace may include measuringprofitability of the online marketplace.

The measurements of the fault diagnosis technique may also include ameasurement of liquidity in the online marketplace, a measurement ofdemand for products and services distributed in the online marketplace,and a measurement of an amount of transactions or subscriptions in theonline marketplace, for example.

Furthermore, in another embodiment, the executing at least one of theprocedures and the measurements of the fault diagnosis technique mayinclude determining cause of an issue. The determination of the cause ofan issue in the online marketplace may be at least based on anevaluation of historical information regarding the online marketplace,the issue, and/or a related issue or marketplace, for example.

In one embodiment, the MDF may classify a marketplace with respect toits current health state (e.g., with respect to its currenteffectiveness, efficiency, profitability, or liquidity). Also, the MDFmay classify a marketplace with respect to its health state based ontransactions within a period of time. The MDF may also quantify risk andimpact of a marketplace within its current health state, or with respectto a period of time and/or quantity of transactions within that periodof time. These quantifications may facilitate prioritization ofdiagnostic procedures, measurements, and/or indicators of a faultdiagnosis technique.

Further, the MDF may select an action (e.g., a remedy) associated with adiagnosis, for example, to improve the state of a marketplace. The MDFmay also provide to a user, another system, or itself, target levels,vital measurements, and reports on progress of actions to improve thestate of a marketplace. Furthermore, the aforementioned may be providedvia fault diagnosis and known probabilistic models.

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments.Subject matter may, however, be embodied in a variety of different formsand, therefore, covered or claimed subject matter is intended to beconstrued as not being limited to any example embodiments set forthherein; example embodiments are provided merely to be illustrative.Likewise, a reasonably broad scope for claimed or covered subject matteris intended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

The terminology used in the specification is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of example embodiments of the invention. In general,terminology may be understood at least in part from usage in context.For example, terms, such as “and”, “or”, or “and/or,” as used herein mayinclude a variety of meanings that may depend at least in part upon thecontext in which such terms are used. Typically, “or” if used toassociate a list, such as A, B or C, is intended to mean A, B, and C,here used in the inclusive sense, as well as A, B or C, here used in theexclusive sense. In addition, the term “one or more” as used herein,depending at least in part upon context, may be used to describe anyfeature, structure, or characteristic in a singular sense or may be usedto describe combinations of features, structures or characteristics in aplural sense. Similarly, terms, such as “a,” “an,” or “the,” again, maybe understood to convey a singular usage or to convey a plural usage,depending at least in part upon context. In addition, the term “basedon” may be understood as not necessarily intended to convey an exclusiveset of factors and may, instead, allow for existence of additionalfactors not necessarily expressly described, again, depending at leastin part on context.

Likewise, it will be understood that when an element is referred to asbeing “connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between” versus “directly between”, “adjacent” versus “directlyadjacent”, etc.).

It will be further understood that the terms “comprises,” “comprising,”“includes” and/or “including,” when used herein, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof, and in the following description, the samereference numerals denote the same elements.

Now, in order to more specifically describe example embodiments of thepresent invention, various embodiments of the present invention will bedescribed in detail with reference to the attached drawings. However,the present invention is not limited to the example embodiments, but maybe embodied in various forms. In addition, the detailed is not intendedas an extensive or detailed discussion of known concepts. As such,details that are known generally to those of ordinary skill in therelevant art may have been omitted or may be handled in summary fashion.

While example embodiments have been particularly shown and describedwith reference to FIGS. 1-3 it will be understood by one of ordinaryskill in the art that various changes in form and details may be madetherein without departing from the spirit and scope of exampleembodiments, as defined by the following claims.

FIG. 1 illustrates a block diagram of one embodiment of a network 100that can implement one embodiment of the MDF. As shown, FIG. 1, forexample, the network 100 includes a variety of networks, such as localarea local area network (LAN)/wide area network (WAN) 105 and wirelessnetwork 110, a variety of devices, such as client device 101 and mobiledevices 102-104, and a variety of servers, such as application servers107-109 and search server 106.

A network, such as the network 100, may couple devices so thatcommunications may be exchanged, such as between a server and a clientdevice or other types of devices, including between wireless devicescoupled via a wireless network, for example. A network may also includemass storage, such as network attached storage (NAS), a storage areanetwork (SAN), or other forms of computer or machine readable media, forexample. A network may include the Internet, one or more local areanetworks (LANs), one or more wide area networks (WANs), wire-line typeconnections, wireless type connections, or any combination thereof.Likewise, sub-networks may employ differing architectures or may becompliant or compatible with differing protocols, may interoperatewithin a larger network. Various types of devices may, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router mayprovide a link between otherwise separate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a telephone line or link,for example.

A wireless network, such as wireless network 110, may couple clientdevices with a network. A wireless network may employ stand-alone ad-hocnetworks, mesh networks, Wireless LAN (WLAN) networks, cellularnetworks, or the like. A wireless network may further include a systemof terminals, gateways, routers, or the like coupled by wireless radiolinks, or the like, which may move freely, randomly or organizethemselves arbitrarily, such that network topology may change, at timeseven rapidly. A wireless network may further employ a plurality ofnetwork access technologies, including Long Term Evolution (LTE), WLAN,Wireless Router (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or4G) cellular technology, or the like. Network access technologies mayenable wide area coverage for devices, such as client devices withvarying degrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

Signal packets communicated via a network, such as a network ofparticipating digital communication networks, may be compatible with orcompliant with one or more protocols. Signaling formats or protocolsemployed may include, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX,Appletalk, or the like. Versions of the Internet Protocol (IP) mayinclude IPv4 or IPv6.

The Internet refers to a decentralized global network of networks. TheInternet includes local area networks (LANs), wide area networks (WANs),wireless networks, or long haul public networks that, for example, allowsignal packets to be communicated between LANs. Signal packets may becommunicated between nodes of a network, such as, for example, to one ormore sites employing a local network address. A signal packet may, forexample, be communicated over the Internet from a user site via anaccess node coupled to the Internet. Likewise, a signal packet may beforwarded via network nodes to a target site coupled to the network viaa network access node, for example. A signal packet communicated via theInternet may, for example, be routed via a path of gateways, servers,etc. that may route the signal packet in accordance with a targetaddress and availability of a network path to the target address.

FIG. 2 illustrates a block diagram of one embodiment of an electronicdevice 200 that can implement an aspect of one embodiment of the MDF.Instances of the electronic device 200 may include servers, such asservers 106-109, and client devices, such as client devices 101-104. Aclient device may be a desktop computer, a laptop computer, a tablet, ora smartphone, for example. In general, the electronic device 200 caninclude a processor 202, memory 210, a power supply 206, andinput/output components, such as network interface(s) 230, an audiointerface 232, a display 234, a key pad or keyboard 236, an input/outputinterface 240, and a communication bus 204 that connects theaforementioned elements of the electronic device. The network interfaces230 can include a receiver and a transmitter (or a transceiver), and anantenna for wireless communications. The processor 202 can be one ormore of any type of processing device, such as a central processing unit(CPU). Also, for example, the processor 202 can be central processinglogic; central processing logic includes hardware, firmware, softwareand/or combinations of each to perform a function(s) or an action(s),and/or to cause a function or action from another component. Also, basedon a desired application or need, central processing logic may include asoftware controlled microprocessor, discrete logic such as anapplication specific integrated circuit (ASIC), aprogrammable/programmed logic device, memory device containinginstructions, or the like, or combinational logic embodied in hardware.Also, logic may also be fully embodied as software. The memory 210,which can include RAM 212 or ROM 214, can be enabled by one or more ofany type of memory device, such as a primary (directly accessible by theCPU) and/or a secondary (indirectly accessible by the CPU) storagedevice (e.g., flash memory, magnetic disk, optical disk). The RAM caninclude an operating system 221, data storage 224, and applications 222,such as an embodiment the MDF 223. Further description of operation ofone embodiment of the MDF 223 will be provided below in conjunction withFIG. 3. The ROM can include BIOS 220 of the electronic device 200. Thepower supply 206 contains one or more power components, and facilitatessupply and management of power to the electronic device 200. Theinput/output components can include any interfaces for facilitatingcommunication between any components of the electronic device 200,components of external devices (such as components of other devices ofthe network 100), and end users. For example, such components caninclude a network card that is an integration of a receiver, atransmitter, and one or more I/O interfaces. A network card, forexample, can facilitate wired or wireless communication with otherdevices of a network. In cases of wireless communication, an antenna canfacilitate such communication. Also, the I/O interfaces, can includeuser interfaces such as monitors, keyboards, touchscreens, microphones,and speakers. Further, some of the I/O interfaces and the bus 204 canfacilitate communication between components of the electronic device200, and in one embodiment can ease processing performed by theprocessor 202.

Where the electronic device 200 is a client device, it can include acomputing device capable of sending or receiving signals, such as via awired or a wireless network. A client device may, for example, include adesktop computer or a portable device, such as a cellular phonetelephone, a smart phone, a display pager, a radio frequency (RF)device, an infrared (IR) device, a Personal Digital Assistant (PDA), ahandheld computer, a tablet computer, a laptop computer, a set top box,a wearable computer, an integrated device combining various features,such as features of the forgoing devices, or the like.

Also, a client device may vary in terms of capabilities or features.Claimed subject matter is intended to cover a wide range of potentialvariations. For example, a cell phone embodiment may include a numerickeypad or a display of limited functionality, such as a monochromeliquid crystal display (LCD) for displaying text. In contrast, however,as another example, a web-enabled client device may include one or morephysical or virtual keyboards, mass storage, one or more accelerometers,one or more gyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, or a display with a high degree offunctionality, such as a touch sensitive color 2D or 3D display, forexample.

Further, a client device may include or may execute a variety ofoperating systems, including a personal computer operating system, suchas a Windows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like. A client device may include ormay execute a variety of possible applications, such as a clientsoftware application enabling communication with other devices, such ascommunicating one or more messages regarding operation or configurationof the MDF. A client device may also include or execute an applicationto communicate content related to the MDF, such as, for example, textualcontent, multimedia content, or the like. A client device may alsoinclude or execute an application to perform a variety of possibletasks, such as browsing, searching, or analyzing forms of contentrelated to the MDF.

Where the electronic device 200 is a server, it can include a computingdevice that is capable of sending or receiving signals, such as via awired or wireless network, or may be capable of processing or storingsignals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like.

Further, a server may vary widely in configuration or capabilities, butgenerally, a server may include one or more central processing units andmemory. A server may also include one or more mass storage devices, oneor more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like. Particularly, the server may be an applicationserver that includes a configuration to provide an application, such asone embodiment of the MDF, via a network to another device. Also, anapplication server may, for example, host a website that can provide auser interface for one embodiment of the MDF.

Further, an application server may provide a variety of services thatinclude web services, third-party services, audio services, videoservices, email services, instant messaging (IM) services, SMS services,MMS services, FTP services, voice over IP (VOIP) services, calendaringservices, photo services, or the like, all of which may work inconjunction with the MDF. Examples of content provide by theabovementioned applications, including one embodiment of the MDF, mayinclude text, images, audio, video, or the like, which may be processedin the form of physical signals, such as electrical signals, forexample, or may be stored in memory, as physical states, for example.

FIG. 3 illustrates a flowchart of a method performed by an electronicdevice, such as a computer, implementing an example embodiment of theMDF (method 300). In short, the method 300 may analyze and manage anonline marketplace; and besides a computer, the method may be performedby a variety of electronic devices, such an application server or, ingeneral, the device of FIG. 2. Further, in one embodiment, a processor(e.g., the processor 202) can perform the method 300 by executingprocessing device readable instructions encoded in memory (e.g., thememory 210). In such an embodiment, the instructions encoded in memorymay include example aspects of the MDF mentioned below.

The method 300 begins with a first aspect of the MDF searching for,identifying, and evaluating an appropriate fault diagnosis technique foran online marketplace (at block 302). A fault diagnosis technique mayinclude various procedures, measurements, and indicators for determininghealth of a marketplace and diagnosing issues with respect to health ofa marketplace. Further, the fault diagnosis technique may include use ofvarious statistical models, such as Bayesian Nets and Markov Processes.These and other possible statistical models may be part of one or moreanalytics libraries, for example.

Further, in performing method 300, the MDF may initiate the method withrespect to a set of known features, factors, and states related to theMDF-managed online marketplace. Known features may include profitmargins and liquidity measurements, for example.

The method 300 then with a second aspect of the MDF, prioritizesprocedures, measurements, and/or indicators of the appropriate faultdiagnosis technique according to the evaluation at 302 (at block 304);and a third aspect activates the procedures and/or the measurements ofthe appropriate fault diagnosis technique (at block 306). For example,the second aspect may determine critical indicators for determiningissues or the overall health of a marketplace. Further, the secondaspect may then rank these indicators, and procedures and measurementsfor identifying and measuring such indicators, by their effectivenessand/or efficiency, for example. The procedures may include proceduresfor classifying issues and the overall health state of the marketplace.For example, various measurements can be made in addition to standardtechniques, and their metrics may be combined in an analysis of themarketplace. Examples of measurements may include measurements ofindicators, such as liquidity of the marketplace, demand for productsand services distributed in the marketplace, and/or prices of suchproducts and services, all of which may be critical indicators. Themeasurements may also include or relate to measurements of otherindicators, which may be critical indicators too, such as an amount oftransactions or subscriptions in a marketplace, and measurementsregarding efficiency of clearance of surplus products and services. Forexample, a measurement may include tracking fulfillment of products forsale online via an online advertising link.

Measurements may also include measurements of subscription rates,liquidity, monetization yield for sellers (or publishers) and/or networkof sellers, quality of demand fulfillment, quality of matching, and enduser activity for long and/or short term engagements. Measurements ofsubscription rates may include measurements of supply by sellers anddemand by buyers in the marketplace. This measurement may includemeasurements of committed inventory from sellers or spending by buyers,for example. Measurements of liquidity may include measurements ofreliability and frequency of transactions in the marketplace, which maybe indicated by volume of transactions and speed of these transactions,for example. Measurements of monetization yield for sellers may includemeasurements of variance in rates controlled by the marketplace andrates controlled by alternative marketplaces. Measurements of quality ofdemand fulfillment may include inquiring about allocation of adinventory, price paid with respect to buyer expectations, and value asindicated in a purchase, for example. Measurements of quality ofmatching may include measuring appropriate distance metrics with respectto a space of subscribed demand and classified inventory. Measurementsof end user activity for long and/or short term engagements may includemeasurements of session length, visitation frequency, depth of servicesinvoked, and footprints of data generated within a network. This may bemeasured to monitor impact of matching inventory supply and advertisingdemand, for example.

With respect to classifying issues and the overall health state of themarketplace, classifying a condition of a marketplace may includeidentifying the most relevant states of an online marketplace. In otherwords, the states that have the most material impact on profit, demand,or liquidity, for example. Such identifying may include unstructuredlearning (e.g., non-parametric clustering, or infinite mixture models)to determine an appropriate amount of states to consider, and toidentify the states in particular.

In one embodiment, once sufficient factors and conditions areidentified, the identification and prioritization of an appropriatefault diagnosis technique and aspects of the technique can utilizesupervised learning to build classification models for assigning ahealth state to an online marketplace or an aspect of the marketplace.Also, the MDF can utilize Bayesian Nets and/or Markov Processes forexample, to determine casual influence flows and independency structuresinherent in various factors and conditions. With results of suchmodeling, as well as other techniques described herein, the MDF mayidentify value in a new feature or aspect of an online marketplace.Additionally, diagnosing the new feature may be prioritized over thediagnosis of other features in the marketplace.

In supervised learning, a dataset containing pre-classified objects maybe provided to aide in model generation. A resulting model may then beused to classify subsequent objects, such as subsequent aspects andstates of a marketplace. In contrast, un-supervised learning does notuse known information on pre-classified objects.

The method 300 then with a fourth aspect of the MDF performs theappropriate fault diagnosis for the online marketplace (at block 308).Performing a fault diagnosis technique may include identifying ordetermining the nature and/or cause of an issue in an onlinemarketplace, such as an error or inefficiency in the marketplace. Theissue may be diagnosed by the fourth aspect of the MDF through anevaluation of historical information regarding the marketplace, theissue, and/or related marketplaces or issues. The diagnosis may alsoinclude an examination of an online marketplace with respect to aspectsof the marketplace related to the issue. Also, a diagnosis may include acomparison of results of an examination of an online marketplace and theevaluation of respective historical information.

For example, the fault diagnosis technique may identify latencies in anonline marketplace (or internet service for example). These latenciesmay then be correlated to overall health of the online marketplace,where the correlations may be based on historical information gatheredfrom previously ran fault diagnosis, for example. Further, theselatencies, for example, can facilitate determining the nature and causeof one or more issues in an online marketplace, respectively. Also, sucha determination may be based on historical information, for example; andsuch historical information may be stored in a data repository (e.g.,relational database) of the MDF, for example. Also, the fourth aspectmay run a fault diagnosis technique or aspects of a fault diagnosistechnique on aspects of an online marketplace or systems connected tothe marketplace.

With respect to the overall health of the online marketplace, with thediagnosis of issue(s), the fourth aspect of the MDF, for example, mayclassify the marketplace with respect to the marketplace's health (e.g.,error rate, effectiveness, and/or efficiency). Also, the fourth aspect,for example, may quantify risk and impact of the marketplace's health.As described herein, the MDF may then select an action (e.g., remedy),to reduce risk and impact issues associated with a health classificationof the marketplace (or diagnosis of the marketplace).

The method 300 then with a fifth aspect of the MDF stores results of theexecution of the appropriate fault diagnosis technique (at block 310).Further, the fifth aspect of the MDF may store an effectiveness ratingof the one or more of the techniques, procedures, or measurementsdiscovered by the first aspect. The fifth aspect may also store a faultdiagnosis profile that may include a particular prioritization of theprocedures of the fault diagnosis technique, for example. The profilemay also include results of one or more diagnoses using the faultdiagnosis technique with a particular prioritization, and otherinformation related to the fault diagnosis technique with the particularprioritization. This other information may include effectiveness of thefault diagnosis technique with the particular prioritization.Furthermore, another aspect of the MDF may rerun a fault diagnosistechnique periodically, and store the results and effectiveness ratingsof one or more of the procedures of that technique to determine andtrack overall effectiveness of the technique through various periods oftime. Also, the periodic performance of the fault diagnosis technique aswell as the other aspects of the MDF may be automated with little to nooperator or user interaction.

With respect to method 300, blocks 302 through 310 can be performedonline, offline, or via a combination thereof. For example, 302 through310, which results in a profile, can be performed offline.

Then, at block 312, a remedy for a diagnosed issue may be generated by asixth aspect of the MDF, according to at least the stored results ofblock 310. The remedy may be generated online for example, based on theprofile. The remedy may also be based on historical information relatedto remedying the diagnosed issue. Further, the remedy may be based on agroup of the profiles. In one embodiment, multiple profiles that arerelated may be categorized and grouped in a categorical profile, forexample. In such an embodiment, the sixth aspect may generate a remedybased on the categorical profile or an aspect of the categoricalprofile, for example.

Additionally, with generation of the remedy, the sixth aspect may alsogenerate thresholds, benchmarks, and target measurements to facilitatedetermining progress of the remedy. For example, where a remedy hasmultiple stages (e.g., multiple chronological stages), the remedy mayinclude one or more deadlines (e.g., a specific deadline or range oftime for a deadline) and/or one or more measurement thresholds orbenchmarks for one or more of the multiple stages.

Also, a remedy may include transition paths for guiding a marketplacefrom one state or condition to another state or condition. Further, aremedy may be transformed into a switch of a control system for anonline marketplace, where the switch may be continuous or discrete inits control of an aspect of an online marketplace. A remedy may alsoinclude multifaceted procedures that include various switches; and suchprocedures may vary over time with respect to changes in marketconditions and an online marketplace.

The method 300 then with a seventh aspect of the MDF executes the remedy(at block 314). For example, the remedy is executed online.

The method 300 then with an eighth aspect of the MDF analyzes theeffectiveness of the remedy and stores the analysis (at block 316). Theanalysis of the effectiveness of the remedy may be performed offline,for example, after an evaluation of a remedied issue. The eighth aspectmay also rank the remedies for a particular issue by effectiveness, forexample.

Finally, the method 300 at block 318, with a ninth aspect of the MDFprovides feedback with respect to the diagnosis technique and the remedyto respective aspects of the MDF (such as an analysis of aneffectiveness of a remedy to the sixth aspect). Using this feedback, thesixth aspect may update its generation of that remedy. Also, the method300 with the ninth aspect or another aspect of the MDF may providefeedback to the fifth aspect, for example. Using this feedback, thefifth aspect may update a particular profile associated with a diagnosistechnique associated with the remedy. Further, besides theaforementioned feedback, the ninth aspect, for example, may providereports and recommendations to users or operators of the MDF or anonline marketplace. These reports may include information for improvingthe online market marketplace or remedying issues within or related tothe marketplace. Such reports may include recommended remedies orfurther areas to investigate, for example.

Additionally, considering the abovementioned feedback and continuousrunning of the method 300, the MDF may generate risk profiles withrespect to various states of an online marketplace over various timeperiods. Also, such feedback allows for the MDF to remain flexible tomake adaptations with respect to changes in conditions and structure ofan online marketplace.

From the foregoing, it can be seen that the present invention provides adiagnostics system for an online marketplace analogous to a system fordiagnosing a patient seeking healthcare. Further, the diagnosticssystem, in addition or in conjunction with another system may provideremedial actions once a diagnosis is made. Further, provided is a systemfor deriving the diagnostics system, wherein generating and selectingprocedures for the diagnostics system includes statistical modeling ofthe procedures to determine their effectiveness in making diagnoses.

As it can be imagined, there are various embodiments for providing andderiving the marketplace diagnostics framework described herein. It istherefore intended that the foregoing detailed description be regardedas illustrative rather than limiting, and that it be understood that itis the following claims, including all equivalents, that are intended todefine the spirit and scope of this invention.

We claim:
 1. A method comprising: automatically identifying, by aprocessing unit, a fault diagnosis technique for analyzing a state of anonline service, the analyzing comprising detecting a latency issue inthe online service and using an end user engagement measurement tomeasure an impact of matching inventory supply and advertising demand;executing, by the processing unit, the fault diagnosis technique todetermine the state of the online service, determination of the state ofthe online service comprising: detecting the latency issue in the onlineservice, determining the end user engagement measurement, anddetermining a measurement of the impact of matching inventory supply andadvertising demand using the determined end user engagement measurement;storing, by the processing unit, diagnostic information about the stateof the online service, the diagnostic information including informationabout the detected latency issue, the end user engagement measurementand the measurement of the impact of matching inventory supply andadvertising demand on the online service determined using the end userengagement measurement; determining, by the processing unit, a remedyfor the state of the online service and the detected latency issue usingat least the diagnostic information indicating the detected latencyissue, the remedy comprising at least one transition path to guide theonline service from one state to another state; executing, by theprocessing unit, the determined remedy for the state of the onlineservice and the detected latency issue, the determined remedy beingexecuted to repair the state of the online service and the detectedlatency issue currently existing in the online service; and evaluating,by the processing unit, remediation information resulting from theexecution of the determined remedy, evaluation of the remediationinformation comprising evaluating an effectiveness of the determinedremedy for the state of the online service and the detected latencyissue.
 2. The method of claim 1, further comprising: identifying, by theprocessing unit and using the detected latency, a cause of the detectedlatency issue; and the determining the remedy further comprisingdetermining the remedy based on the identified cause for the detectedlatency issue.
 3. The method of claim 2, historical information gatheredfrom at least one previous analysis of the online service is used inidentifying the cause of the detected latency issue.
 4. The method ofclaim 1, the remedy comprising at least one transition path to guide theonline service from one condition to another condition.
 5. The method ofclaim 1, the fault diagnosis technique comprising a number of proceduresand measurements for use in analyzing the online service to detect thelatency issue in the online service.
 6. The method of claim 1, furthercomprising: evaluating, by the processing unit, the fault diagnosistechnique to determine an effectiveness of the fault diagnosis techniquein analyzing the online service for the latency issue.
 7. The method ofclaim 6, evaluating the fault diagnosis technique to determine aneffectiveness of the fault diagnosis technique in analyzing the onlineservice further comprising evaluating a number of procedures andmeasures of the fault diagnosis technique to determine an effectivenessof the number of procedures and measures of the fault diagnosistechnique in analyzing the online service for the latency issue.
 8. Themethod of claim 1, the remedy comprising a switch of a control systemfor the online service.
 9. The method of claim 8, the switch comprisingcontinuous control of at least one aspect of the online service.
 10. Themethod of claim 8, the switch providing a discrete control of at leastone aspect of the online service.
 11. The method of claim 1, the remedycomprising a number of procedures having a number of switchescontrolling at least one aspect of the online service.
 12. The method ofclaim 11, the number of procedures of the remedy vary over time withrespect to changes in at least one condition of the online service. 13.A non-transitory computer-readable storage medium tangibly encoded withcomputer-executable instructions that when executed by a processorassociated with a computing device perform a method comprising:identifying a fault diagnosis technique for analyzing a state of anonline service, the analyzing comprising detecting a latency issue inthe online service and using an end user engagement measurement tomeasure an impact of a matching inventory supply and advertising demand;executing the fault diagnosis technique to determine the state of theonline service, determination of the state of the online servicecomprising: detecting the latency issue in the online service,determining the end user engagement measurement, and determining ameasurement of the impact of matching inventory supply and advertisingdemand using the determined end user engagement measurement; storingdiagnostic information about the state of the online service, thediagnostic information comprising information about the detected latencyissue, the end user engagement measurement and the measurement of theimpact of matching inventory supply and advertising demand on the onlineservice determined using the end user engagement measurement;determining a remedy for the state of the online service and thedetected latency issue using at least the diagnostic informationindicating the detected latency issue, the remedy comprising at leastone transition path to guide the online service from one state toanother state; executing the determined remedy for the state of theonline service and the detected latency issue, the determined remedybeing executed to repair the state of the online service and thedetected latency issue currently existing in the online service; andevaluating remediation information resulting from the execution of thedetermined remedy, evaluation of the remediation information comprisingevaluating an effectiveness of the determined remedy for the onlinestate of the online service and the detected latency issue.
 14. Thenon-transitory computer-readable storage medium of claim 13, furthercomprising: identifying, using the detected latency, a cause of thedetected latency issue; and the determining the remedy furthercomprising determining the remedy based on the identified cause for thedetected latency issue.
 15. The non-transitory computer-readable storagemedium of claim 14, historical information gathered from at least oneprevious analysis of the online service is used in identifying the causeof the detected latency issue.
 16. The non-transitory computer-readablestorage medium of claim 13, the fault diagnosis technique comprising anumber of procedures and measurements for use in analyzing the onlineservice to detect the latency issue in the online service.
 17. Thenon-transitory computer-readable storage medium of claim 13, furthercomprising: evaluating, by the processing unit, the fault diagnosistechnique to determine an effectiveness of the fault diagnosis techniquein analyzing the online service for the latency issue.
 18. Thenon-transitory computer-readable storage medium of claim 17, evaluatingthe fault diagnosis technique to determine an effectiveness of the faultdiagnosis technique in analyzing the online service further comprisingevaluating a number of procedures and measures of the fault diagnosistechnique to determine an effectiveness of the number of procedures andmeasures of the fault diagnosis technique in analyzing the onlineservice for the latency issue.
 19. A computing device comprising: aprocessor; a non-transitory storage medium for tangibly storing thereonprogram logic for execution by the processor, the program logiccomprising: identifying logic executed by the processor for identifyinga fault diagnosis technique for analyzing a state of an online service,the analyzing comprising detecting a latency issue in the online serviceand using an end user engagement measurement to measure an impact ofmatching inventory supply and advertising demand; executing logicexecuted by the processor for executing the fault diagnosis technique todetermine the state of the online service, determination of the state ofthe online service comprising: detecting the latency issue in the onlineservice, determining the end user engagement measurement, anddetermining a measurement of the impact of matching inventory supply andadvertising demand using the determined end user engagement measurement;storing logic executed by the processor for storing diagnosticinformation about the state of the online service, the diagnosticinformation including information about the detected latency issue, theend user engagement measurement and the measurement of the impact ofmatching inventory supply and advertising demand on the online servicedetermined using the end user engagement measurement; determining logicexecuted by the processor for determining a remedy for the state of theonline service and the latency issue using at least the diagnosticinformation indicating the detected latency issue, the remedy comprisingat least one transition path to guide the online service from one stateto another state; executing logic executed by the processor forexecuting the determined remedy for the state of the online service andthe detected latency issue, the determined remedy being executed torepair the state of the online service and the detected latency issuecurrently existing in the online service; and evaluating logic executedby the processor for evaluating remediation information resulting fromthe execution of the determined remedy, evaluation of the remediationinformation comprising evaluating an effectiveness of the determinedremedy for the state of the online service and the detected latencyissue.